In recent years, climate change concerns, federal/state initiatives, and other factors have driven a rapid rise in the installation of renewable energy generation (EG) systems (i.e., systems that generate energy using renewable resources such as solar, wind, hydropower, etc.) at residential and non-residential sites. Solar photovoltaic (PV) systems, in particular, have been very popular EG systems.
PV-based EG systems have continued to improve as new innovations lead to lower manufacturing and installation costs, higher solar panel efficiencies, and greater control over energy distribution. Despite these improvements, the power output for PV-based EG systems remain susceptible to bad weather conditions, including storm systems and dense cloud cover. Bad weather conditions can greatly reduce an amount of sunlight that reaches PV-based EG systems, which can adversely affect their corresponding power generation.
Therefore, knowing beforehand which EG sites will be affected by an impending storm or dense cloud cover can be very advantageous, as preemptive actions can be taken to reduce the storms negative effects on power production. However, conventional methods of storm forecasting for downstream EG sites are typically inaccurate, costly, and involve indirect calculations, broad assumptions, and imprecise estimations. As such, better forecasting and control systems are needed.